In recent years the machine learning community has been dominated by the need for accuracy, and a competitive nature has spread throughout the field, with internationally open challenges (asking participants to improve on a baseline system of a particular database), such as the Interspeech Computational Paralinguistics Challenges [1]} and the ACM Audio / Visual Emotion Challenges [2]}, assisting in the rapid advancements of approaches across multiple domains. Although healthy competitions and high accuracy has its advantages, one limitation to this is the lack of explainabiltiy across the field [3]}. It is of up most importance that such machine learning models are interpretable, offering a clear use-case, as ultimately, without this, the results alone are potentially meaningless [4]}. As machine learning is predominately a pattern recognition task, visualisation of data has been a key enhancement for system explainabiltiy, particularly in deep learning, as this allows for trends within data to be more easily understood and interpreted [5]}.
